期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
卷 42, 期 3, 页码 291-312出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCC.2011.2157494
关键词
Classification; decision-tree induction; evolutionary algorithms (EAs); regression; soft computing
类别
资金
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)
This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.
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